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Future Internet 2018, 10(6), 54; https://doi.org/10.3390/fi10060054

StegNet: Mega Image Steganography Capacity with Deep Convolutional Network

1
,
1
and
1,2,*
1
School of Computer Science, Shanghai University, Shanghai 200444, China
2
Shanghai Institute for Advanced Communication & Data Science, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Received: 6 May 2018 / Revised: 10 June 2018 / Accepted: 12 June 2018 / Published: 15 June 2018
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Abstract

Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only 0.76% of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis. View Full-Text
Keywords: convolutional neural network; image steganography; steganography capacity convolutional neural network; image steganography; steganography capacity
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Wu, P.; Yang, Y.; Li, X. StegNet: Mega Image Steganography Capacity with Deep Convolutional Network. Future Internet 2018, 10, 54.

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